RNA performs and regulates a diverse range of cellular processes, with new functional roles being uncovered at a rapid pace. Interest is growing in how these functions are linked to RNA structures that form in the complex cellular environment. A growing suite of technologies that use advances in RNA structural probes, high-throughput sequencing and new computational approaches to interrogate RNA structure at unprecedented throughput are beginning to provide insights into RNA structures at new spatial, temporal and cellular scales.
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Differential analysis of RNA structure probing experiments at nucleotide resolution: uncovering regulatory functions of RNA structure
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The authors thank A. Coraor, A. Silverman and R. Batey for informative discussions about the chemical kinetic view of reactivities and K. Watters and P. Carlson for assistance with graphics. The authors also thank D. Mathews and C. Khosla for inspiring the connections between the chemical and statistical perspectives of reactivities and V. Gopalan for historical perspectives. They also thank M. Evans for helpful comments on the manuscript. This work was supported by an Arnold and Mabel Beckman Foundation Postdoctoral Fellowship (to E.J.S.), the Tri-Institutional Training Program in Computational Biology and Medicine (via a National Institutes of Health training grant T32GM083937 to A.M.Y.), the National Institute of General Medical Sciences of the National Institutes of Health (grant numbers 1DP2GM110838 and R01GM120582 to J.B.L.) and Searle Funds at the Chicago Community Trust (to J.B.L.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Nature Reviews Genetics thanks P. Bevilacqua, L. Ritchey, C. Douds, A. Laederach and the other anonymous reviewer(s) for their contribution to the peer review of this work.
The authors declare no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
- Secondary structures
The patterns of base pairing interactions in RNA that create helices, loops, bulges, junctions and single-stranded regions. In addition to Watson–Crick base pairs, RNAs can pair in many non-canonical patterns.
- High-throughput sequencing
(HTS). A suite of technologies that can be used to sequence millions to billions of DNA molecules simultaneously. Many experiments can be performed at once because bioinformatics tools can be used to distinguish sequence signals between experiments.
A measure of a chemical probing reaction that contains RNA structural information. Typically, high reactivities indicate unstructured regions, while low reactivities indicate structured regions.
- Reverse transcription
(RT). The process by which RNA is enzymatically converted into complementary DNA. RT proceeds along the RNA template in the 3′ to 5′ direction.
- Chemical probes
Small molecules that chemically react with RNA molecules in a structure-dependent fashion. Reactions produce adducts that can be detected to give a measure of the structure of an RNA.
- Watson–Crick face
The edges of RNA bases that form the canonical adenine–uracil and guanine–cytosine base pairs.
- Selective 2′-hydroxyl acylation analysed by primer extension
(SHAPE). A technique that uses a class of chemical probes that modifies the RNA backbone. SHAPE probes can be used to interrogate RNA structure at single-nucleotide resolution.
- Tertiary structure
The orientation of secondary structure elements and nucleotides that gives rise to sophisticated three-dimensional structures. Tertiary structures can be stabilized by non-covalent interactions and divalent cations.
An event in which reverse transcriptase stops when encountering a chemical probe adduct on an RNA, producing a truncated cDNA that can be used to map the adduct position.
An event in which reverse transcriptase produces a mutation when encountering a chemical probe adduct on an RNA. This mutation can be used to map the adduct position.
- Pseudo-free energy
An energy term introduced into RNA folding algorithms that incorporates chemical probing data to more accurately model RNA structures. It is not a rigorously derived thermodynamic free energy but captures the relationship between high reactivities corresponding to unpaired regions, hence the use of ‘pseudo’ in its name.
A measure of accuracy of RNA structure prediction equal to the number of true positive pairs predicted divided by the sum of true positive and false negative pairs predicted. Sensitivity is often used in combination with positive predictive value to assess the predictive accuracy of RNA structure models.
- Positive predictive value
(PPV). A measure of accuracy of RNA structure prediction equal to the number of true positive pairs predicted divided by the sum of true positive and false positive pairs predicted. PPV is often used in combination with sensitivity to assess the predictive accuracy of RNA structure models. PPV is equivalent to one minus the false discovery rate.
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Strobel, E.J., Yu, A.M. & Lucks, J.B. High-throughput determination of RNA structures. Nat Rev Genet 19, 615–634 (2018). https://doi.org/10.1038/s41576-018-0034-x
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